Traffic Sign Recognition using Convolutional Neural Networks YOLOv3 Algorithm-Reference-Cited by-同舟云学术

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Traffic Sign Recognition using Convolutional Neural Networks YOLOv3 Algorithm

Published:2023-10-18 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS)
language:
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Author:

A Gnanabaskaran¹,S Pranav Ram K²,N Vaishnavi²,D Shabari Prasannaa²

Affiliation:

1. K S Rangasamy College of Technology,Faculty of Computer Science and Engineering,Department of Computer Science and Engineering,Tiruchengode,India

2. K S Rangasamy College of Technology,Student of Computer Science and Engineering,Department of Computer Science and Engineering,Tiruchengode,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10330805/10331621/10331850.pdf?arnumber=10331850

Reference24 articles.

1. Various Generative Adversarial Networks Model for Synthetic Prohibitory Sign Image Generation

2. Real-time railroad track components inspection based on the improved YOLOv4 framework

3. Efficient Traffic Sign Recognition Using Cross-Connected Convolution Neural Networks Under Compressive Sensing Domain

4. YOLOv4: optimal speed and accuracy of object detection;Bochkovskiy;arXiv,2020

5. Using Deep Learning in Infrared Images to Enable Human Gesture Recognition for Autonomous Vehicles

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